Improving collaboration between palliative care and neurology: Focus on patient, family & healthcare provider experience
Bibliographic record
Abstract
Abstract Background The prevalence of neurodegenerative diseases is increasing as the population ages. As these diseases are often incurable, progressive, and associated with significant loss of ability to communicate over time, it is paramount that advanced care planning (ACP) take place early in the disease trajectory to facilitate goal‐concordant care. There is increasing recognition of the barriers for neurologists to facilitate ACP and end‐of‐life care. Palliative care is a medical speciality that focuses on improving quality of life for patients living with serious illness, including helping with healthcare planning and decision‐making that align with the individual’s values and goals. However, there is a paucity of literature on how to best integrate palliative care or which elements of an integrated neuro‐palliative approach are the most beneficial to patients, caregivers, and clinicians. Our study aims to assess the experience of an embedded pilot clinic between Neurology and Palliative Care at the University Health Network (UHN) Memory Clinic on patients/caregivers and health care practitioners. Methods We aim to recruit 60 participants, including patients and family members, seen at the UHN Supportive Care Memory Clinic, and who screen positive for three of seven triggers for palliative care involvement for neurological patients as proposed by the National End of Life Care Programme of National Health Service in England. ACP conversations will follow an evidence‐based semi‐structured conversation guide. The impact of the clinic will be evaluated using patient, caregiver, and both Neurology and Palliative Care team member experience surveys. Secondary measures will include the percentage change in ACP and Power of Attorney documentation pre and post initiation of the clinic. Results From clinic inception in June 2020, 19 patients were seen (all‐virtual). Due to the COVID‐19 pandemic, the clinic was paused for the majority of 2021. Our study received UHN Quality Improvement approval in March 2021, and we began enrolling patients into our study in November 2021; enrolment is ongoing. Conclusions The results of this study will serve as a guide for quality improvement of our existing clinic and can inform future implementation of scalable integrated models of care between palliative care and neurology, and for other subspecialties and centres.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".